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AI Product Engineer Port RM

Valsoft Corporation

Beirut, Beirut Governorate, Lebanon Hybrid permanent

Posted: February 17, 2026

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Quick Summary

We're looking for an AI Product Engineer to join our team in Beirut, Lebanon, where we operate and manage software companies, delivering mission-critical solutions across multiple verticals.

Required Skills

Job Description

Aspire Software is looking for an AI Product Engineer to join our team in Lebanon.

Here is a little window into our company: Aspire Software operates and manages wholly owned software companies, providing mission-critical solutions across multiple verticals. By implementing industry best practices, Aspire delivers a time sensitive integration process, and the operation of a decentralized model has allowed it to become a hub for creating rapid growth by reinvesting in its portfolio.

About the role:

We’re building AI startups inside an established portfolio of vertical software companies—and we need engineers who can make ideas real, fast.

Our mandate is to partner with Fluent’s existing businesses to create new AI-powered products and sell them into their verticalized customer bases—and once proven, expand beyond them. The starting point could be either: agentifying an existing workflow that the business software already helps facilitate today, or building a net-new product altogether. The through-line is simple—we’re creating customer value in one of two forms: helping them operate more efficiently, or helping them capture revenue opportunities they’re currently missing.

Key Responsibilities

• Rapidly prototype AI-powered products and features: take a concept from your Ventures Lead and deliver a working, demoable solution in 2–3 days.
• • Build end-to-end: frontend, backend, API integrations, and database—whatever it takes to get a prototype in front of a customer.
• • Integrate AI model APIs (OpenAI, Anthropic, Gemini, Azure) and build agentic workflows using function calling, tool use, and orchestration frameworks.
• • Build RAG pipelines that ground AI in domain-specific customer data using vector databases and embedding models.
• Partner with portfolio company R&D teams to take validated prototypes into production—translating your fast-build into their architecture, standards, and infrastructure.
• • Participate in customer demos and POCs alongside your Ventures Lead, providing technical depth and fielding implementation questions.

Full-Stack Fundamentals

• Comfortable building end-to-end: modern frontend frameworks (React, Vue.js, TypeScript), server-side development (Node.js, Python, or C#/.NET), REST/GraphQL APIs, and relational databases (PostgreSQL, MySQL).
• You don’t need to be an expert in every layer, but you need to be able to stand up a working, demoable product without waiting on another team.

AI & Agentic Development

• Hands-on experience integrating AI model APIs (OpenAI, Anthropic, Gemini, Azure Cognitive Services) into real products not just toy projects.
• Experience building agentic workflows: multi-step AI agents using function calling, tool use, and orchestration frameworks (LangChain, CrewAI, AutoGen, Semantic Kernel).
• Strong RAG skills: building pipelines that ground AI in domain-specific customer data using vector databases (Pinecone, Weaviate) and embedding models.
• Fluency in prompt engineering, token/cost management, and latency tradeoffs in production AI features.

Tools & Velocity

• Proficiency with AI-enhanced development tools (Cursor, Windsurf, Claude Code, bolt.new, v0, Replit)—and the instinct to adopt whatever’s next.
• Git, CI/CD, and agile workflows. You ship, you iterate, you don’t over-process.


Requirements:
• 1-4 years of professional experience in software engineering, applied AI, or product development but what you’ve built matters more than tenure.
• Demonstrated ability to take an idea from concept to working prototype independently.
• Deep fluency in the AI tooling ecosystem and the habit of staying current. You know about new releases before anyone asks you to look.
• Clear communicator who can explain technical decisions to non-technical stakeholders and collaborate effectively with engineers across teams.
• A track record of self-directed work: side projects, open-source contributions, hackathon wins, or entrepreneurial ventures carry significant weight.
• Bonus: experience in SaaS, vertical market software, or early-stage startup environments.

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